As discussed in [1]}, reconstruction and forecasting-based approaches have shown to be, so far, the best candidates for anomaly detection in time-series. While reconstruction-based methods allow modeling inconsistencies within the global distribution of time series, forecasting-based approaches are more appropriate for capturing local anomalies.
For that reason, Zhao et al. [1]} have introduced a hybrid method leveraging these two complementary paradigms. Specifically, they design a two-stream attention-based graph network that simultaneously optimizes forecasting and reconstruction losses.
